Functional prototype for estimating surgery rooms’ usage time with data mining techniques

The goal of this project is to create a stand-alone prototype for optimizing operating room administration utilizing data mining techniques. The project will collect historical data on operating room utilization and train a machine-learning model that can forecast the time required for each type of...

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Autores:: Amado Cáceres, Daniel Fernando

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2023

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: spa

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dc.title.spa.fl_str_mv	Functional prototype for estimating surgery rooms’ usage time with data mining techniques
dc.title.translated.spa.fl_str_mv	Prototipo funcional para estimar el tiempo de uso de quirófanos con técnicas de minería de datos
title	Functional prototype for estimating surgery rooms’ usage time with data mining techniques
spellingShingle	Functional prototype for estimating surgery rooms’ usage time with data mining techniques Systems engineer Technological innovations Operating room Usage time Scheduling Data mining Data analysis Machine learning Algorithms Surgery Ingeniería de sistemas Innovaciones tecnológicas Minería de datos Aprendizaje automático Algoritmos Cirugía Quirófano Tiempo de uso Programación lineal Análisis de datos
title_short	Functional prototype for estimating surgery rooms’ usage time with data mining techniques
title_full	Functional prototype for estimating surgery rooms’ usage time with data mining techniques
title_fullStr	Functional prototype for estimating surgery rooms’ usage time with data mining techniques
title_full_unstemmed	Functional prototype for estimating surgery rooms’ usage time with data mining techniques
title_sort	Functional prototype for estimating surgery rooms’ usage time with data mining techniques
dc.creator.fl_str_mv	Amado Cáceres, Daniel Fernando
dc.contributor.advisor.none.fl_str_mv	Talero Sarmiento, Leonardo Hernán Martínez Cáceres, Elkin Yesid Moreno Corzo, Feisar Enrique
dc.contributor.author.none.fl_str_mv	Amado Cáceres, Daniel Fernando
dc.contributor.cvlac.spa.fl_str_mv	Talero Sarmiento, Leonardo Hernán [0000031387] Moreno Corzo, Feisar Enrique [0001499008]
dc.contributor.googlescholar.spa.fl_str_mv	Moreno Corzo, Feisar Enrique [es&oi=ao]
dc.contributor.orcid.spa.fl_str_mv	Talero Sarmiento, Leonardo Hernán [0000-0002-4129-9163] Moreno Corzo, Feisar Enrique
dc.contributor.researchgate.spa.fl_str_mv	Talero Sarmiento, Leonardo Hernán [Leonardo_Talero]
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de Investigación Tecnologías de Información - GTI
dc.contributor.apolounab.spa.fl_str_mv	Talero Sarmiento, Leonardo Hernán [Leonardo_Talero] Moreno Corzo, Feisar Enrique [feisar-enrique-moreno-corzo]
dc.subject.keywords.spa.fl_str_mv	Systems engineer Technological innovations Operating room Usage time Scheduling Data mining Data analysis Machine learning Algorithms Surgery
topic	Systems engineer Technological innovations Operating room Usage time Scheduling Data mining Data analysis Machine learning Algorithms Surgery Ingeniería de sistemas Innovaciones tecnológicas Minería de datos Aprendizaje automático Algoritmos Cirugía Quirófano Tiempo de uso Programación lineal Análisis de datos
dc.subject.lemb.spa.fl_str_mv	Ingeniería de sistemas Innovaciones tecnológicas Minería de datos Aprendizaje automático Algoritmos Cirugía
dc.subject.proposal.spa.fl_str_mv	Quirófano Tiempo de uso Programación lineal Análisis de datos
description	The goal of this project is to create a stand-alone prototype for optimizing operating room administration utilizing data mining techniques. The project will collect historical data on operating room utilization and train a machine-learning model that can forecast the time required for each type of surgical operation using a data-driven approach. The model proposed originates from the analysis of different data management algorithms in order to obtain different approaches to the same problem and to validate the accuracy and reliability of these algorithms in the surgery time optimization.
publishDate	2023
dc.date.issued.none.fl_str_mv	2023-05-30
dc.date.accessioned.none.fl_str_mv	2024-01-29T14:01:10Z
dc.date.available.none.fl_str_mv	2024-01-29T14:01:10Z
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/bachelorThesis
dc.type.local.spa.fl_str_mv	Trabajo de Grado
dc.type.coar.none.fl_str_mv	http://purl.org/coar/resource_type/c_7a1f
dc.type.hasversion.none.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.redcol.none.fl_str_mv	http://purl.org/redcol/resource_type/TP
format	http://purl.org/coar/resource_type/c_7a1f
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/20.500.12749/23309
dc.identifier.instname.spa.fl_str_mv	instname:Universidad Autónoma de Bucaramanga - UNAB
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional UNAB
dc.identifier.repourl.spa.fl_str_mv	repourl:https://repository.unab.edu.co
url	http://hdl.handle.net/20.500.12749/23309
identifier_str_mv	instname:Universidad Autónoma de Bucaramanga - UNAB reponame:Repositorio Institucional UNAB repourl:https://repository.unab.edu.co
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Abedini, A., Li, W., & Ye, H. (2017). An Optimization Model for Operating Room Scheduling to Reduce Blocking Across the Perioperative Process. Procedia Manufacturing, 10, 60–70. https://doi.org/10.1016/j.promfg.2017.07.022 Ahmed, A., He, L., Chou, C., & Hamasha, M. M. (2021). A prediction-optimization approach to surgery prioritization in operating room scheduling. Journal of Industrial and Production Engineering, 39(5), 399–413. https://doi.org/10.1080/21681015.2021.2017362 Allen, J. (2018, October 5). Optimizing Surgical Block Time. The Hospital Medical Director. https://hospitalmedicaldirector.com/optimizing-surgical-block-time/ Burgette, L. F., Mulcahy, A. W., Mehrotra, A., Ruder, T., & Wynn, B. O. (2017). Estimating Surgical Procedure Times Using Anesthesia Billing Data and Operating Room Records. Health Services Research, 52(1), 74–92. https://doi.org/10.1111/1475-6773.12474 Chiang, A. J., Jeang, A., Chiang, P. C., Chiang, P. S., & Chung, C.-P. (2019). Multi-objective optimization for simultaneous operating room and nursing unit scheduling. International Journal of Engineering Business Management, 11, 184797901989102. https://doi.org/10.1177/1847979019891022 Chu, J., Hsieh, C.-H., Shih, Y.-N., Wu, C.-C., Singaravelan, A., Hung, L.-P., & Hsu, J.-L. (2022). Operating Room Usage Time Estimation with Machine Learning Models. Healthcare, 10(8), 1518. https://doi.org/10.3390/healthcare10081518 Coban, E., Kayış, E., & Dexter, F. (2022). The effect of few historical data on the performance of sample average approximation method for operating room scheduling. International Transactions in Operational Research, 30(1), 126–150. https://doi.org/10.1111/itor.13101 Hosseini, N., Sir, M. Y., Jankowski, C. J., & Pasupathy, K. S. (2015). Surgical Duration Estimation via Data Mining and Predictive Modeling: A Case Study. AMIA ... Annual Symposium Proceedings. AMIA Symposium, 2015(2), 640–648. https://pubmed.ncbi.nlm.nih.gov/26958199/ IBM Cloud Education. (2021, January 15). What Is Data Mining? Www.ibm.com. https://www.ibm.com/cloud/learn/data-mining Jeroen. (2021, March 2). The Why, What, and How of Operating Room Efficiency. DEO.care. https://deo.care/the-why-what-and-how-of-operating-room-efficiency/ Levine, W. C., & Dunn, P. F. (2015). Optimizing Operating Room Scheduling. Anesthesiology Clinics, 33(4), 697–711. https://doi.org/10.1016/j.anclin.2015.07.006 Li, Q., Liu, Y., Sipahi Döngül, E., Yang, Y., Ruan, X., & Enbeyle, W. (2022). Operating Room Planning for Emergency Surgery: Optimization in Multiobjective Modeling and Management from the Latest Developments in Computational Intelligence Techniques. Computational Intelligence and Neuroscience, 2022(PMC8872665), 1–14. https://doi.org/10.1155/2022/2290644 Lin, Y.-K., & Li, M.-Y. (2021). Solving Operating Room Scheduling Problem Using Artificial Bee Colony Algorithm. Healthcare, 9(2), 152. https://doi.org/10.3390/healthcare9020152 Maghzi, P., Mohammadi, M., Pasandideh, S. H. R., & Naderi, B. (2022). Operating Room Scheduling Optimization Based on a Fuzzy Uncertainty Approach and Metaheuristic Algorithms. International Journal of Engineering, 35(2), 258–275. https://doi.org/10.5829/ije.2022.35.02b.01 Morgenthaler, S. (2009). Exploratory data analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 1(1), 33–44. https://doi.org/10.1002/wics.2 Osman, A. S. (2019). Data Mining Techniques: Review. International Journal of Data Science Research, 2(1), 1–5. http://ojs.mediu.edu.my/index.php/IJDSR/article/view/1841/717 Romero, C., & Ventura, S. (2012). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27. https://doi.org/10.1002/widm.1075 Saleh, B. B., Saleh, G. B., & Barakat, O. (2020). Operating Theater Management System: Block-Scheduling. Artificial Intelligence and Data Mining in Healthcare, 83–98. https://doi.org/10.1007/978-3-030-45240-7_5 Salud, M. (2022, August 29). Marco Legal Colombiano - Acreditación en Salud. Acreditación En Salud. https://acreditacionensalud.org.co/marco-legal-colombiano/ Santoso, L. W., Sudiarso, A., Masruroh, N. A., & Herliansyah, M. K. (2018). Cluster analysis to determine the priority of operating room scheduling. AIP Conference Proceedings. https://doi.org/10.1063/1.5042914 Sanyal, N. (2022, April 29). Why focus on operating room prime time utilization? LeanTaaS. https://leantaas.com/blog/optimizing-your-operating-rooms-prime-time-utilization/ TİMUÇİN, T., & BİROĞUL, S. (2021). Operating Room Scheduling by Using Hybrid Genetic Algorithm. Düzce Üniversitesi Bilim ve Teknoloji Dergisi. https://doi.org/10.29130/dubited.946453 Wu, X., & Xiao, X. (2018, March 31). Optimizing the Three-stage Operating Room Scheduling Problem with RVNS-GA. IEEExplore; University of Science and Technology Bejing. https://ieeexplore-ieee-org.aure.unab.edu.co/stamp/stamp.jsp?tp=&arnumber=8377551&tag=1 Xiang, W. (2017). A multi-objective ACO for operating room scheduling optimization. Natural Computing, 16(4), 607–617. https://doi.org/10.1007/s11047-016-9607-9 Xiao, Y., & Yoogalingam, R. (2022, September 22). A simulation optimization approach for planning and scheduling in operating rooms for elective and urgent surgeries. ScienDirect. https://www.sciencedirect.com/science/article/pii/S2211692322000273 Zhang, D., Liu, Y., M’Hallah, R., & Leung, S. C. H. (2010). A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems. European Journal of Operational Research, 203(3), 550–558. https://doi.org/10.1016/j.ejor.2009.09.014 Choi, Sangdo, & Wilhelm, Wilbert E. (2014). On capacity allocation for operating rooms. Computers & Operations Research, 44, 174-184, ISSN 0305-0548, Elsevier BV, <https://doi.org/10.1016/j.cor.2013.11.007> Luo, Yan Yan, & Wang, Bing (2019). A New Method of Block Allocation Used in Two-Stage Operating Rooms Scheduling. IEEE Access, 7, 102820-102831, ISSN 2169-3536, Institute of Electrical and Electronics Engineers (IEEE), <https://doi.org/10.1109/access.2019.2926780> Zheng, Qian, Shen, Jie, Liu, Ze-qing, Fang, Kai, & Xiang, Wei (2011). Resource allocation simulation on operating rooms of hospital. 2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management, IEEE, <https://doi.org/10.1109/icieem.2011.6035502> Abedini, Amin, Li, Wei, & Ye, Honghan (2017). An Optimization Model for Operating Room Scheduling to Reduce Blocking Across the Perioperative Process. Procedia Manufacturing, 10, 60-70, ISSN 2351-9789, Elsevier BV, <https://doi.org/10.1016/j.promfg.2017.07.022> Wang, Zhengli, & Dexter, Franklin (2022). More accurate, unbiased predictions of operating room times increase labor productivity with the same staff scheduling provided allocated hours are increased. Perioperative Care and Operating Room Management, 29, 100286, ISSN 2405-6030, Elsevier BV, <https://doi.org/10.1016/j.pcorm.2022.100286> Maghzi, P., Mohammadi, M., Pasandideh, S. H. R., & Naderi, B. (2022). Operating Room Scheduling Optimization Based on a Fuzzy Uncertainty Approach and Metaheuristic Algorithms. International Journal of Engineering, 35(2), 258-275, ISSN 1728-144X, International Digital Organization for Scientific Information (IDOSI), <https://doi.org/10.5829/ije.2022.35.02b.01> TİMUÇİN, Tunahan, & BİROĞUL, Serdar (2021). Operating Room Scheduling by Using Hybrid Genetic Algorithm. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, ISSN 2148-2446, Duzce Universitesi Bilim ve Teknoloji Dergisi, <https://doi.org/10.29130/dubited.946453> Deshpande, Vinayak, Mundru, Nishanth, Rath, Sandeep, Knowles, Martyn, Rowe, David, & Wood, Benjamin (2021). Data-Driven Surgical Tray Optimization to Improve Operating Room Efficiency. SSRN Electronic Journal, ISSN 1556-5068, Elsevier BV, <https://doi.org/10.2139/ssrn.3866226>
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dc.publisher.grantor.spa.fl_str_mv	Universidad Autónoma de Bucaramanga UNAB
dc.publisher.faculty.spa.fl_str_mv	Facultad Ingeniería
dc.publisher.program.spa.fl_str_mv	Pregrado Ingeniería de Sistemas
institution	Universidad Autónoma de Bucaramanga - UNAB
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spelling	Talero Sarmiento, Leonardo Hernán52f3ced8-d447-4a4d-a30c-74958c9587aaMartínez Cáceres, Elkin Yesid1190addd-8ba5-4961-bb3a-0a65f53852c0Moreno Corzo, Feisar Enriqueee761f02-1ce9-473f-b811-9b495af86e41Amado Cáceres, Daniel Fernando23e830ae-a185-4efb-898f-f5a9a0bf554bTalero Sarmiento, Leonardo Hernán [0000031387]Moreno Corzo, Feisar Enrique [0001499008]Moreno Corzo, Feisar Enrique [es&oi=ao]Talero Sarmiento, Leonardo Hernán [0000-0002-4129-9163]Moreno Corzo, Feisar EnriqueTalero Sarmiento, Leonardo Hernán [Leonardo_Talero]Grupo de Investigación Tecnologías de Información - GTITalero Sarmiento, Leonardo Hernán [Leonardo_Talero]Moreno Corzo, Feisar Enrique [feisar-enrique-moreno-corzo]ColombiaUNAB Campus Bucaramanga2024-01-29T14:01:10Z2024-01-29T14:01:10Z2023-05-30http://hdl.handle.net/20.500.12749/23309instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coThe goal of this project is to create a stand-alone prototype for optimizing operating room administration utilizing data mining techniques. The project will collect historical data on operating room utilization and train a machine-learning model that can forecast the time required for each type of surgical operation using a data-driven approach. The model proposed originates from the analysis of different data management algorithms in order to obtain different approaches to the same problem and to validate the accuracy and reliability of these algorithms in the surgery time optimization.INTRODUCTION 1. PROBLEM STATEMENT 2. RESEARCH OBJECTIVES 2.1. GENERAL AIM 2.2. SPECIFIC OBJECTIVES 3. JUSTIFICATION 4. REFERENTIAL FRAMEWORK 4.1. CONCEPTUAL FRAMEWORK 4.2. THEORETICAL FRAMEWORK 4.3. STATE OF ART 4.4. LEGAL FRAMEWORK 5. METHODOLOGY 6. EXPECTED RESULTS 7. PROTOTYPE FOR THE OPTIMIZATION OF TIME MANAGEMENT IN OPERATING ROOMS 7.1. CHARACTERIZATION OF THE ALLOCATION OF OPERATING ROOMS 7.1.1. Characteristics and protocols of operating rooms 7.1.2. Criteria and methods used in operating room allocation 7.2. SOFTWARE COMPONENTS FOR THE DECISION-MAKING MODEL 7.2.1. Prototype software requirements 7.2.1.1. Functional requirements 7.2.1.2. Non-functional requirements 7.2.2. Case and user diagram 7.2.3. Data description and characterization 7.2.4. Data preparation and cleaning, exploratory data analysis 7.2.5. Model delineation 7.3. DATA-DRIVEN DECISION-MAKING MODEL 7.3.1. Real model 7.3.2. Modified model 7.4. REMARKABLE INSIGHTS AND ANALYSIS OF SYSTEM EVALUATION RESULTS 8. CONCLUSIONS 9. RECOMMENDATIONS AND FUTURE WORK BIBLIOGRAPHYPregradoThe goal of this project is to create a stand-alone prototype for optimizing operating room administration utilizing data mining techniques. The project will collect historical data on operating room utilization and train a machine-learning model that can forecast the time required for each type of surgical operation using a data-driven approach. The model proposed originates from the analysis of different data management algorithms in order to obtain different approaches to the same problem and to validate the accuracy and reliability of these algorithms in the surgery time optimization.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Functional prototype for estimating surgery rooms’ usage time with data mining techniquesPrototipo funcional para estimar el tiempo de uso de quirófanos con técnicas de minería de datosIngeniero de SistemasUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaPregrado Ingeniería de Sistemasinfo:eu-repo/semantics/bachelorThesisTrabajo de Gradohttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/TPSystems engineerTechnological innovationsOperating roomUsage timeSchedulingData miningData analysisMachine learningAlgorithmsSurgeryIngeniería de sistemasInnovaciones tecnológicasMinería de datosAprendizaje automáticoAlgoritmosCirugíaQuirófanoTiempo de usoProgramación linealAnálisis de datosAbedini, A., Li, W., & Ye, H. (2017). An Optimization Model for Operating Room Scheduling to Reduce Blocking Across the Perioperative Process. Procedia Manufacturing, 10, 60–70. https://doi.org/10.1016/j.promfg.2017.07.022Ahmed, A., He, L., Chou, C., & Hamasha, M. M. (2021). A prediction-optimization approach to surgery prioritization in operating room scheduling. Journal of Industrial and Production Engineering, 39(5), 399–413. https://doi.org/10.1080/21681015.2021.2017362Allen, J. (2018, October 5). Optimizing Surgical Block Time. The Hospital Medical Director. https://hospitalmedicaldirector.com/optimizing-surgical-block-time/Burgette, L. F., Mulcahy, A. W., Mehrotra, A., Ruder, T., & Wynn, B. O. (2017). Estimating Surgical Procedure Times Using Anesthesia Billing Data and Operating Room Records. Health Services Research, 52(1), 74–92. https://doi.org/10.1111/1475-6773.12474Chiang, A. J., Jeang, A., Chiang, P. C., Chiang, P. S., & Chung, C.-P. (2019). Multi-objective optimization for simultaneous operating room and nursing unit scheduling. International Journal of Engineering Business Management, 11, 184797901989102. https://doi.org/10.1177/1847979019891022Chu, J., Hsieh, C.-H., Shih, Y.-N., Wu, C.-C., Singaravelan, A., Hung, L.-P., & Hsu, J.-L. (2022). Operating Room Usage Time Estimation with Machine Learning Models. Healthcare, 10(8), 1518. https://doi.org/10.3390/healthcare10081518Coban, E., Kayış, E., & Dexter, F. (2022). The effect of few historical data on the performance of sample average approximation method for operating room scheduling. International Transactions in Operational Research, 30(1), 126–150. https://doi.org/10.1111/itor.13101Hosseini, N., Sir, M. Y., Jankowski, C. J., & Pasupathy, K. S. (2015). Surgical Duration Estimation via Data Mining and Predictive Modeling: A Case Study. AMIA ... Annual Symposium Proceedings. AMIA Symposium, 2015(2), 640–648. https://pubmed.ncbi.nlm.nih.gov/26958199/IBM Cloud Education. (2021, January 15). What Is Data Mining? Www.ibm.com. https://www.ibm.com/cloud/learn/data-miningJeroen. (2021, March 2). The Why, What, and How of Operating Room Efficiency. DEO.care. https://deo.care/the-why-what-and-how-of-operating-room-efficiency/Levine, W. C., & Dunn, P. F. (2015). Optimizing Operating Room Scheduling. Anesthesiology Clinics, 33(4), 697–711. https://doi.org/10.1016/j.anclin.2015.07.006Li, Q., Liu, Y., Sipahi Döngül, E., Yang, Y., Ruan, X., & Enbeyle, W. (2022). Operating Room Planning for Emergency Surgery: Optimization in Multiobjective Modeling and Management from the Latest Developments in Computational Intelligence Techniques. Computational Intelligence and Neuroscience, 2022(PMC8872665), 1–14. https://doi.org/10.1155/2022/2290644Lin, Y.-K., & Li, M.-Y. (2021). Solving Operating Room Scheduling Problem Using Artificial Bee Colony Algorithm. Healthcare, 9(2), 152. https://doi.org/10.3390/healthcare9020152Maghzi, P., Mohammadi, M., Pasandideh, S. H. R., & Naderi, B. (2022). Operating Room Scheduling Optimization Based on a Fuzzy Uncertainty Approach and Metaheuristic Algorithms. International Journal of Engineering, 35(2), 258–275. https://doi.org/10.5829/ije.2022.35.02b.01Morgenthaler, S. (2009). Exploratory data analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 1(1), 33–44. https://doi.org/10.1002/wics.2Osman, A. S. (2019). Data Mining Techniques: Review. International Journal of Data Science Research, 2(1), 1–5. http://ojs.mediu.edu.my/index.php/IJDSR/article/view/1841/717Romero, C., & Ventura, S. (2012). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27. https://doi.org/10.1002/widm.1075Saleh, B. B., Saleh, G. B., & Barakat, O. (2020). Operating Theater Management System: Block-Scheduling. Artificial Intelligence and Data Mining in Healthcare, 83–98. https://doi.org/10.1007/978-3-030-45240-7_5Salud, M. (2022, August 29). Marco Legal Colombiano - Acreditación en Salud. Acreditación En Salud. https://acreditacionensalud.org.co/marco-legal-colombiano/Santoso, L. W., Sudiarso, A., Masruroh, N. A., & Herliansyah, M. K. (2018). Cluster analysis to determine the priority of operating room scheduling. AIP Conference Proceedings. https://doi.org/10.1063/1.5042914Sanyal, N. (2022, April 29). Why focus on operating room prime time utilization? LeanTaaS. https://leantaas.com/blog/optimizing-your-operating-rooms-prime-time-utilization/TİMUÇİN, T., & BİROĞUL, S. (2021). Operating Room Scheduling by Using Hybrid Genetic Algorithm. Düzce Üniversitesi Bilim ve Teknoloji Dergisi. https://doi.org/10.29130/dubited.946453Wu, X., & Xiao, X. (2018, March 31). Optimizing the Three-stage Operating Room Scheduling Problem with RVNS-GA. IEEExplore; University of Science and Technology Bejing. https://ieeexplore-ieee-org.aure.unab.edu.co/stamp/stamp.jsp?tp=&arnumber=8377551&tag=1Xiang, W. (2017). A multi-objective ACO for operating room scheduling optimization. Natural Computing, 16(4), 607–617. https://doi.org/10.1007/s11047-016-9607-9Xiao, Y., & Yoogalingam, R. (2022, September 22). A simulation optimization approach for planning and scheduling in operating rooms for elective and urgent surgeries. ScienDirect. https://www.sciencedirect.com/science/article/pii/S2211692322000273Zhang, D., Liu, Y., M’Hallah, R., & Leung, S. C. H. (2010). A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems. European Journal of Operational Research, 203(3), 550–558. https://doi.org/10.1016/j.ejor.2009.09.014Choi, Sangdo, & Wilhelm, Wilbert E. (2014). On capacity allocation for operating rooms. Computers & Operations Research, 44, 174-184, ISSN 0305-0548, Elsevier BV, <https://doi.org/10.1016/j.cor.2013.11.007>Luo, Yan Yan, & Wang, Bing (2019). A New Method of Block Allocation Used in Two-Stage Operating Rooms Scheduling. 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SSRN Electronic Journal, ISSN 1556-5068, Elsevier BV, <https://doi.org/10.2139/ssrn.3866226>https://apolo.unab.edu.co/en/persons/leonardo-taleroORIGINALTesis.pdfTesis.pdfTesisapplication/pdf724174https://repository.unab.edu.co/bitstream/20.500.12749/23309/2/Tesis.pdf473667b2749e5f7b33ef25b82ab2bb4aMD52open access2024_Licencia.pdf2024_Licencia.pdfLicenciaapplication/pdf358859https://repository.unab.edu.co/bitstream/20.500.12749/23309/6/2024_Licencia.pdf205aefaa93df6c6be09a70b5fb2ba6a4MD56metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8829https://repository.unab.edu.co/bitstream/20.500.12749/23309/5/license.txt3755c0cfdb77e29f2b9125d7a45dd316MD55open accessTHUMBNAILTesis.pdf.jpgTesis.pdf.jpgIM Thumbnailimage/jpeg5063https://repository.unab.edu.co/bitstream/20.500.12749/23309/7/Tesis.pdf.jpg0e6dde4b1ffd77bd7a2eaa41cb9d899eMD57open access2024_Licencia.pdf.jpg2024_Licencia.pdf.jpgIM Thumbnailimage/jpeg9759https://repository.unab.edu.co/bitstream/20.500.12749/23309/8/2024_Licencia.pdf.jpgb228356a8a1318dadb29cdd4767724e1MD58metadata only access20.500.12749/23309oai:repository.unab.edu.co:20.500.12749/233092024-04-25 17:45:04.797open accessRepositorio Institucional \| Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.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

Functional prototype for estimating surgery rooms’ usage time with data mining techniques

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